Semantic segmentation is a fundamental computer vision task that assigns every pixel of an image with category labels. Accompanied by the development [1]}, [2]}, [3]}, [4]}, [5]} of deep learning, the semantic segmentation has also evolved tremendously under the supervised learning paradigm [6]}, [7]}, [8]}. However, unlike common image-level datasets such as ImageNet-1K/ImageNet-22K image classification which are easily scaled up to tens of thousands of categories, existing semantic segmentation tasks involve usually up to tens or hundreds of categories due to the significantly higher annotation cost, and thus limit the segmenters' capability in handling rich semantics. This issue can be alleviated by few-shot/one-shot/zero-shot algorithms [9]}, [10]}, [11]}, [12]}, and this paper focuses on the most basic zero-shot settings which aim to segment novel categories that have no single segmentation annotation.
